An aging population faces significant healthcare challenges and for senior citizens it is a growing concern worldwide. In the United States of America, 13% of the population is over age 65, a percentage which is projected to reach 19% by 2030. More than one third of senior citizens live alone which creates significant challenges in the healthcare space. For example, a lack of visibility regarding the well-being of senior citizens and an inability to quickly respond to their needs will burden the existing healthcare system if the current trajectory continues. Falls are known to be the primary reason of injury related death for senior citizens and the second reason of injury related death for persons of all ages. Immediate treatment of falls is required to increase the life span of an elderly patient and to reduce long-term treatment costs. Falls can occur under several scenarios, for example, falls from walking or standing, falls from standing on supports such as ladders, etc., falls from sleeping or lying in a bed, falls from sitting on a chair, falls due to obstacles, etc.
Conventional wearable fall determination systems comprising, for example, accelerometers, posture sensors, global positioning system devices, mechanical and sound alarms, etc., are typically wrapped around a user's body and are required to be worn throughout the day and night, which is intrusive and inconvenient to the user. If a user chooses not to wear a fall determination system around the user's body at all times, and the user encounters a fall or any other incident when the fall determination system is not worn, the fall could lead to a severe injury or death. Moreover, the majority of the conventional wearable fall determination systems require a user to push a panic alert button, if the user needs help or has fallen. When the user pushes the panic alert button on a conventional fall determination system, the conventional fall determination system transmits a signal to a predetermined receiver over a network. The receiver then assists the user until, for example, a medical professional, a medical assistant, an emergency responder, etc., arrives at the user's location. Users who experience a severe fall may not be able to press the panic alert button after the fall and hence cannot alert a respondent. Furthermore, conventional fall determination systems do not detect falls in real time, or predict a risk of a fall.
Other conventional fall determination systems that are non-wearable eliminate a few of the drawbacks of the conventional wearable fall determination systems. However, most of the conventional non-wearable fall determination systems utilize a single sensor to detect a fall and fail to handle errors leading to erroneous alerts to caretakers. The conventional fall determination systems with a single sensor have a low signal to noise ratio and are more prone to erroneous readings resulting in generation of false warning alerts. Moreover, the conventional fall determination systems do not monitor the health status of the user and do not alert caretakers when the health status of the user declines which is an early sign of a fall.
Some conventional systems utilize thermal cameras to determine body temperature maps. However, thermal cameras are expensive and require calibration. Conventional passive infrared based motion detectors are based on detecting temperature changes in a field of view. However, these conventional passive infrared based motion detectors do not detect stationary objects and are not reliable for fall determination. Furthermore, while conventional systems detect falls by calculating data related to the falls and comparing that data to various thresholds, these conventional systems fail to differentiate a fall that could lead to an injury or death from a typical fall related to a regular activity performed by the user, for example, falling onto a couch to rest.
Hence, there is a long felt need for a method and a non-intrusive biomechanical parameter determination system operably coupled to multiple sensors of different types for identifying target objects, for example, humans, stationary objects, etc., determining biomechanical parameters, for example, a fall, posture, walking speed, positions, acceleration, etc., of one or more of the target objects, for example, elderly persons and other users in a region in real time, differentiating between different types of falls, determining the severity levels of the falls, and enhanced alerting without requiring the target objects to press a panic alert button. Furthermore, there is a need for a method and a biomechanical parameter determination system for monitoring the health status of a target object to identify symptoms or a risk of a fall in advance for advanced emergency alerting and intervention before irreversible deterioration or a fall occurs, thereby improving response time and chances of a full recovery.